Optimal distortion measures for the high rate vector quantization of LPC parameters

Linear predictive coding (LPC) parameters are widely used in various speech coding applications for representing the short-time spectral envelope information of speech [1]. For low bit rate speech coding applications, it is important to quantize these parameters using as few bits as possible. Considerable workhas been done in the past to develop both scalar and vector quantization procedures to quantize the LPC parameters [2, 3,4]. Scalar quantizers quantize each of the LPC parameters independently, while vector quantizers consider the entire set of LPC parameters as an entity and allow for direct minimization of quantization distortion. Because of this, the vector quantizers result in smaller distortion than the scalar quantizers at any given bit rate. The vector quantizers, however, have one major problem; their computational complexity is high. In our earlier paper [3], we have reported on a vector quantizer where the LPC parameter vector is split in the line spectral frequency (LSF) domain to overcome this complexity problem. We have shown that this quantizer can quantize the LPC parameters at 24 bits/frame with an average spectral distortion of 1 dB, less than 2% frames having spectral distortionl in the range 2-4 dB and no frame having spectral distortion greater than 4 dB.

The authors explore the benefits of time-varying bit allocation to excitation and LPC (linear predictive coding) parameters for the case of codebook-excited LPC. The overall bit rate in the experiment was 4.8, 6.4, or 8.0 kb/s. In each case, permissible bit rates for the LPC component were 0, 24, 36, or 48 bits per frame, one of which was selected for each speech frame using a brute-force search maximum performance. Average SNR gains over conventional time-invariant methods were modest, on the order of 1 to 2 dB, but gains for certain speech segments were as high as 3 to 5 dB. Perceptually, gains due to variable bit allocation were most noticeable in the 6.4 kb/s system, especially with female speakers. However, even in this case, the benefits of flexible bit allocation were somewhat offset by distortions due to other inadequacies in the coding algorithm. >

An elegant relationship (here called symmetry) between the linear predictive coding (LPC) analysis and the composite sinusoidal modeling (CSM) analysis is described. The objective is to provide a systematic view of the theory of voice spectral modeling such as LPC, PARCOR, CSM, and line spectral pairs (LSP). All of these methods of analysis are reduced to the problems of deriving the model parameters for lower‐order autocorrelation functions equal to the number of degrees of freedom of the model. From the point of view of the theory of orthogonal polynomials, LPC and CSM are the theory of orthogonal polynomials of a unit circle (in the case of LPC) and on the real axis (in the case of CSM). Beautiful symmetries hold in regard to the formulation, definition of various parameters, and solution algorithm. Also, a new interpretation is given here for the LSP from the point of view of orthogonal polynomials. © 2002 Scripta Technica, Electron Comm Jpn Pt 3, 85(6): 42–54, 2002

A modified regular-pulse excitation (RPE) linear predictive coding (LPC) codec is used to encode audio signals of 15 kHz bandwidth. With appropriate perceptual masking and carefully selected excitation structure, a fixed-pulse excitation (FPE) LPC codec is able to provide high-fidelity audio output. The quantization of both the LPC parameters and the excitation pulse sequence is examined. It is found that the LPC parameters can be quantized efficiently by adaptive differential PCM, while the excitation pulse sequences need more bits to quantize the amplitudes compared to narrowband speech encoding. Informal listening tests show that high-fidelity audio can be achieved at bit rates less than 100 kb/s.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An algorithm for LPC (linear predictive coding) parameter optimization in multipulse (MP)-LPC based speech coders is presented. It is shown that, by taking into account the nature of the MP-excitation signal into LPC parameter computation, it is possible to improve the effectiveness of the LPC model. This results in a better quality of the reconstructed signal in terms both of objective and subjective criteria. The implementation details of the algorithm are discussed and experimental results are presented. In particular a comparison with standard MP-LPC techniques is given. >

A class of reverberant speech enhancement techniques involve processing of the linear prediction residual signal following Linear Predictive Coding (LPC). These approaches are based on the assumption that reverberation is mainly confined to the prediction residual and affects the LPC coefficients to a lesser extent. This chapter begins with a study on the effects of reverberation on the LPC parameters where mathematical tools from statistical room acoustics are used in the analysis. Consequently, a general framework for dereverberation using LPC is formulated and several existing methods utilizing this approach are reviewed. Finally, a specific method for processing a reverberant prediction residual is presented in detail. This method uses a combination of spatial averaging and larynx cycle-based temporal averaging. Experiments with a microphone array in a small office demonstrate the dereverberation and noise suppression of the spatiotemporal averaging method, showing up to a 5 dB improvement in segmental SRR and 0.33 in the normalized Bark spectral distortion score.

A cascade formant model is well suited to describe certain speech segments, such as vowels and vowel-like sounds. The formant model is also useful because the relationship between formants and the vocal tract configurations are well understood; however, this model is not adequate for other speech sounds, such as stops, fricatives, nasals, etc. On the other hand, LPC (linear predictive coding) analysis, of sufficiently high order, can adequately describe the spectrum of any speech sound, but the relationship between the LPC parameters and the spectrum or vocal tract configuration is not obvious. This paper describes a speech analysis/synthesis scheme that uses both formants and LPC parameters for different sections of a speech signal. Thus, in some regions, the benefit of the formant model can be utilized, while in other regions, the LPC representation can be used to obtain a good description of the speech spectrum. The analysis algorithm resolves the problem of discontinuities that arise from using the two different spectral representations. When provided with the same multipulse signal, the method of speech analysis described in this paper produces resynthesized speech of the quality of multipulse LPC.

음성코딩 시 성도는 Linear Predictive Coding (LPC) 계수를 이용해서 모델링 한다. 일반적으로 LPC 계수는 양자화와 선형보간 관점에서 유리한 Line Spectral Frequency (LSF) 파라미터로 변경하여 사용한다. 10차 이상의 다차원 LSF 데이터를 벡터 양자화를 이용하여 직접 코딩하게 되면 벡터 내 상관관계 (intra-frame correlation)를 모두 이용할 수 있으므로 rate-distortion 관점에서는 높은 효율을 기대할 수 있다. 하지만, 계산량과 메모리 요구량이 높아져서 실제 코딩 시스템에서는 사용할 수 없게 되므로, 차원을 나누어 압축하는 Split Vector Quantization (SVQ)이 이용된다. 또한, LSF 데이터는 과거 벡터와의 벡터 간 상관관계 (inter-frame correlation)가 높으므로, 이를 이용한 Predictive Split Vector Quantization (PSVQ)이 사용되고 있다. PSVQ는 SVQ 보다 높은 rate-distortion 성능을 보인다. 본 논문에서는 음성 저장 장치를 위한 최적의 PSVQ를 구현하기 위해서 다수의 과거 프레임 정보와의 벡터 간상관관계 (inter-frame correlation)를 고려한 Multi-Frame AR-model 기반 SVQ (MF-AR-SVQ)를 제안하였다. 기존 PSVQ와 비교해 보았을 때, MF-AR-SVQ는 계산량과 메모리 요구량의 큰 증가 없이, 평균 spectral distortion 관점에서 약 1비트의 성능 향상을 보였다. For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

A novel and improved method and apparatus for quantizing the line spectral pair (LSP) frequencies in a speech compression system is disclosed. A novel and computationally efficient procedure for determining the set of quantization sensitivities for the LSP frequencies is disclosed, which results in a computationally efficient error measure for use in vector quantization of the LSP frequencies. A novel method of weighting the quantization error is disclosed, which accumulates the quantization error in each LSP frequency and weights that error by the sensitivity of that LSP frequency.

A method for generating an artificial conversational speech signal as the input test signal in measuring the characteristics of devices operated by speech signals, such as speech detectors, voice switches, and echo controllers is proposed. A state transition model among talkspurt, pause, double‐talk, and mutual silence in conversational speech signals was introduced to simulate the statistical characteristics of these states in real conversational speech signals. This model assumed that the cumulative distribution of the duration time in each state is exponential. During talkspurt intervals, an artificial voice was generated using a speech synthesizer that controlled the spectrum and source characteristics independently. This artificial signal generation method can generate a signal with given characteristics by changing the values of system parameters, such as the average duration time of each state, transition probability from one state to another, and LPC (linear predictive coding) parameters that expr...

An efficient stochastically excited linear predictive coding algorithm for high quality low bit rate transmission of speech

Recent advances in speech coding algorithms and techniques based on the use of linear prediction now permit high quality voice reproduction at remarkably low bit rates. This paper reviews some of the main ideas underlying the algorithms of major interest today. The concept of removing redundancy by linear prediction is reviewed, first in the context of predictive quantization or DPCM. Then linear predictive coding, adaptive predictive coding, and vector quantization are discussed. The concepts of excitation coding via analysis-by-synthesis linear predictive coding is explained and some important enhancements such as vector sum excitations, and adaptive postfiltering are described. Low-delay coding by backward computation of LPC parameters is explained. The concept of phonetic segmentation of speech for closed-loop coding systems is also presented. Linear prediction is the key technique that underlies almost all of the important algorithms for speech coding of interest today. Finally, we discuss some recent work on nonlinear prediction of speech and its potential for the future of speech coding.

Postfilters for suppressing coding noises in low bit rate speech coders usually operate in the time domain with high complexity and also introduce nonlinear phase distortion to the speech signals because they are recursive filters. In this paper, a frequency-domain postfiltering technique for multiband excited linear predictive (MBELP) coders is proposed. The idea is to firstly sample the postfilter magnitude spectrum at the pitch harmonics and then scale the signal band magnitudes by the sampled postfilter magnitudes. This approach does not need to specifically perform the filtering process, and the signal energies before and after postfiltering can be easily equalized by simply re-scaling the signal band magnitudes. Another obvious advantage of using this frequency-domain postfiltering technique is that the phase information utilized in multiband synthesis will be retained because only the magnitude spectra are modified. In this paper, a fast method is given to sample the postfilter magnitude spectrum which is characterized by line spectral pair (LSP) parameters. The proposed frequency-domain postfilter has a much lower implementation complexity than its time-domain counterpart.

A technique of temporal speech compression, called temporal decomposition, is considered. A mathematical presentation of temporal decomposition is discussed from the point of view of speech production. Temporal decomposition is studied in terms of an iterative solution and its convergence. For vocoder applications, a practical algorithm, short-term temporal decomposition, is proposed. The statistical performance of this algorithm was tested on a multispeaker database. The efficiencies of vector quantization and vector predictability were comparatively studied on data derived from the algorithm and on data from linear predictive coding (LPC) analysis. The results show that data from the algorithm have more efficient information transmission via vector quantization than data from LPC, and that vector prediction coding is feasible for the data from temporal decomposition.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Studying the effect of perturbations on protein structure is a basic approach in protein research. Important problems, such as predicting pathological mutations and understanding patterns of structural evolution, have been addressed by computational simulations that model mutations using forces and predict the resulting deformations. In single mutation-response scanning simulations, a sensitivity matrix is obtained by averaging deformations over point mutations. In double mutation-response scanning simulations, a compensation matrix is obtained by minimizing deformations over pairs of mutations. These very useful simulation-based methods may be too slow to deal with large proteins, protein complexes, or large protein databases. To address this issue, I derived analytical closed formulas to calculate the sensitivity and compensation matrices directly, without simulations. Here, I present these derivations and show that the resulting analytical methods are much faster than their simulation counterparts.